Introduction of the IEEE Guadalajara Section Geoscience and Remote Sensing Society Chapter
Bibliographic record
Abstract
After the 2011 International Geoscience and Remote Sensing Symposium (IGARSS) was held at Vancouver, a discussion among some colleagues from the Western Technological Institute of Superior Studies (ITESO, Instituto Tecnológico y de Estudios Superiores de Occidente) and the National Polytechnic Institute (IPN, Instituto Politécnico Nacional), both from Mexico, showed the necessity to develop a connection between the academic and industrial research communities in remote sensing topics throughout Mexico. The Chapter would offer the opportunity to foster collaboration among the different and research and development projects based on remote sensing techniques, under the leadership of a growing group of professionals in both, the academic and industrial fields. A search process began in order to locate the maximum number of researchers working in remote sensing applications throughout the Mexican territory. With the clear idea to create the Chapter, we were able to meet the minimum requirements set by IEEE, and the formal petition was carried. On October 22nd, 2012, we received the letter from Cecelia Jankowski, Managing Director of Member and Geographic Activities of IEEE, informing us that the requirements of the MGA Board Operations Manual were met, and the IEEE Guadalajara Section Geoscience and Remote Sensing Society Chapter was formed, with a formation date set on October 4th, 2012 with the denomination GRS29 and geocode CH09422.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.157 | 0.093 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".